Experimental demonstration and pan-structurome prediction of climate-associated riboSNitches in Arabidopsis

Background
Genome-Wide Association Studies (GWAS) aim to correlate phenotypic changes with genotypic variation. Single nucleotide variants (SNVs) within transcripts may alter mRNA structure, with potential impacts on transcript stability, macromolecular interactions and translation. However, no plant genomes have been yet assessed for the presence of these structure-altering polymorphisms or “riboSNitches”.
Results
We experimentally demonstrate the presence of riboSNitches in transcripts of two Arabidopsis genes, ZINC RIBBON 3 (ZR3) and COTTON GOLGI-RELATED 3 (CGR3), which are associated with continentality and temperature variation in the natural environment. These riboSNitches are associated with differences in the abundance of their respective transcripts, implying their role in regulating gene expression in adaptation to local climate conditions. We computationally predict transcriptome-wide riboSNitches in 879 naturally inbred Arabidopsis accessions. We also characterize correlations between SNPs/riboSNitches in these accessions and 434 climate descriptors of local environments; suggesting the role of these variants in local adaptation. We integrate this information in CLIMtools V2.0 and provide a new web resource, T-CLIM, which allows users to determine the association of transcript abundance variation with climate variation.
Conclusions
We functionally validate two plant riboSNitches and, for the first time, demonstrate riboSNitch is conditionally dependent on temperature, coining the term conditional riboSNitch. We provide the first pan-genome wide prediction of riboSNitches in plants. We expand our previous CLIMtools web resource with riboSNitch information and with 1868 additional Arabidopsis genomes and 269 additional climate conditions, which will facilitate in silico studies of natural genetic variation, its phenotypic consequences and its role in local adaptation.

Methods
Compiled geo-climatic variables were obtained through curation of databases. We extracted information on 473 climate variables (see description of environmental parameters) for a comprehensive description of the local environment of previously sequenced Arabidopsis accessions [1–7].

For the calculations of genotype × environmental associations (GEA), we used a GWAS approach with each of the numerical environmental parameters included in this study.The online tool GWAPP (http://gwas.gmi.oeaw.ac.at/) was employed using an accelerated mixed model (AMM) [8], which addresses the confounding effects of population stratification, family structure, and cryptic relatedness [9]. This method presents at the same time an issue with the introduction of false negatives. We also include here the results derived from GEA using a linear regression model that does not correct for population structure, also obtained using GWAPP [8].

Transcriptome-wide association (TWA) analysis of correlations between transcript abundance and each of the environmental variables included in this study using the set of 558 accessions within the set of 879 Eurasian accessions with available information on transcript abundance from a previous study [10] (GEO dataset with accession number GSE80744 and SRA study SRP074107). Spearman’s rank correlation coefficients between individual climate variables and individual transcript abundance values were calculated using the correlation function of the Hmisc package in R.

The SNPfold program [11] was used to identify predicted riboSNitches Genome-wide. SNPfold was applied to the 3,830,264 natural variants of protein-coding genes (including introns) within the 879 Eurasian Arabidopsis in our study.

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